1. Field of the Invention
The present invention relates generally to pattern recognition, and more particularly, to correlation filters used in pattern recognition.
2. Description of Related Art
Two-dimensional correlation techniques have used spatial filters (known as correlation filters) to detect, locate and classify targets in observed scenes. A correlation filter should attempt to yield: sharp correlation peaks for targets of interest, high discrimination against unwanted objects, excellent robustness to noise in the input scene and high tolerance to distortions in the input. A variety of filters to address these aspects and other aspects have been proposed (for example, see: B. V. K. Vijaya Kumar, "Tutorial Survey of Composite Filter Designs for Optical Correlators," Applied Optics, Vol. 31, pp. 4773-4801, 1992).
Linear filters known as Synthetic Discriminant Function (SDF) filters have been introduced by Hester and Casasent as well as by Caulfield and Maloney (see: C. F. Hester and D. Casasent, "Multivariant Techniques for Multiclass Pattern Recognition," Applied Optics, Vol. 19, pp. 1758-1761, 1980; H. J. Caulfield and W. T. Maloney, "Improved Discrimination in Optical Character Recognition," Applied Optics, Vol. 8, pp. 2354-2356, 1969).
Other correlation filters include the minimum squared error Synthetic Discriminant Function (MSE SDF) where the correlation filter is selected that yields the smallest average squared error between the resulting correlation outputs and a specified shape (see: B. V. K. Vijaya Kumar, A. Mahalanobis, S. Song, S. R. F. Sims and J. Epperson, "Minimum Squared Error Synthetic Discriminant Functions," Optical Engineering, Vol. 31, pp. 915-922, 1992).
Another filter is the maximum average correlation height (MACH) filter that determines and uses the correlation shape yielding the smallest squared error (see: A. Mahalanobis, B. V. K. Vijaya Kumar, S. R. F. Sims, J. Epperson, "Unconstrained Correlation Filters," Applied Optics, Vol. 33, pp. 3751-3759, 1994). However, the MACH filter and other current filters generally perform only linear operations on input image data and consequently are limited in their performance to detect patterns within the input image data. Moreover, the current approaches suffer the disadvantage of an inadequate ability to process information from multiple sensors as well as at different resolution levels.